Object tracking systems are often used in automotive and industrial settings to identify and follow objects of interest. These systems generally use sensors such as laser rangefinders, radars, or camera systems to identify and track objects. While basic tracking systems can be effective in controlled environments, they are often ineffective in real-world situations such as driving an automobile down a city street where complex and sophisticated object tracking is required.
Visual video tracking allows objects to be tracked using a wide range of modalities including color, shape, or brightness. However, video tracking can be a time-consuming process due to the amount of data that is contained in video. Furthermore, object recognition techniques necessary for tracking are complex and require significant computer processing. To counteract these issues, many video tracking systems only track a single modality such as color while other tracking systems use databases of stored objects. While these systems can accurately track objects under certain conditions, there exists a need for a computationally efficient video tracking system that does not track based on a single modality or rely on a database of stored objects to track a target object.
Modern video tracking systems also make use of algorithms to analyze sequential video frames and track the movement of targets between the frames. Different algorithms have unique strengths and weaknesses, and the choice of algorithm is largely based upon the intended use of the tracking system. Common tracking algorithms are either geared towards target representation and localization or filtering and data association.
Techniques utilizing target representation and localization are generally bottom-up processes with generally low computational complexity. However, these algorithms are primarily used when the camera is static or the tracking is relatively simple. Filtering and data association algorithms are generally top-down processes that incorporate additional factors into the object tracking algorithm. These algorithms are generally more computationally complex and can factor in information about background characteristics, object dynamics, and other features. These methods also are able to handle complex object interaction such as tracking moving objects behind obstructions. The video tracker may also be mounted on a moving foundation while tracking another moving object. However, these filtering and data association methods are extremely complex and require significant computational power.
It would therefore be beneficial for an object tracking system to combine observations from multiple views including various types of visual features to accurately track an object in a wide variety of situations. It would also be beneficial to reduce the number of individual tasks normally associated with complex visual tracking methods and jointly consider the underlying relationships between tasks across different views and different particles to tackle the problem in a unified robust multi-task formulation.